/* --------------------------------------------------------------------------
CppAD: C++ Algorithmic Differentiation: Copyright (C) 2003-17 Bradley M. Bell

CppAD is distributed under multiple licenses. This distribution is under
the terms of the
                    Eclipse Public License Version 1.0.

A copy of this license is included in the COPYING file of this distribution.
Please visit http://www.coin-or.org/CppAD/ for information on other licenses.
-------------------------------------------------------------------------- */
/*
$begin cppad_sparse_hessian.cpp$$
$spell
	ifdef
	ifndef
	colpack
	boolsparsity
	namespace
	Jac
	onetape
	const
	hes
	CppAD
	cppad
	hpp
	bool
	typedef
	endif
	tmp
	std
	var
	cout
	endl
$$

$section CppAD Speed: Sparse Hessian$$
$mindex link_sparse_hessian speed$$


$head Specifications$$
See $cref link_sparse_hessian$$.

$head Implementation$$

$srccode%cpp% */
# include <cppad/cppad.hpp>
# include <cppad/speed/uniform_01.hpp>
# include <cppad/speed/sparse_hes_fun.hpp>

// Note that CppAD uses global_option["memory"] at the main program level
# include <map>
extern std::map<std::string, bool> global_option;

namespace {
	// typedefs
	using CppAD::vector;
	typedef CppAD::AD<double>                     a1double;
	typedef CppAD::AD<a1double>                   a2double;
	typedef vector<bool>                          b_vector;
	typedef vector<size_t>                        s_vector;
	typedef vector<double>                        d_vector;
	typedef vector<a1double>                      a1vector;
	typedef vector<a2double>                      a2vector;
	typedef CppAD::sparse_rc<s_vector>            sparsity_pattern;
	typedef CppAD::sparse_rcv<s_vector, d_vector> sparse_matrix;
	// ------------------------------------------------------------------------
	void create_fun(
		const d_vector&             x        ,
		const s_vector&             row      ,
		const s_vector&             col      ,
		CppAD::ADFun<double>&       fun      )
	{
		// initialize a1double version of independent variables
		size_t n = x.size();
		a1vector a1x(n);
		for(size_t j = 0; j < n; j++)
			a1x[j] = x[j];
		//
		// optimization options
		std::string optimize_options="no_compare_op";
		//
		// order of derivative in sparse_hes_fun
		size_t order = 0;
		//
		if( ! global_option["hes2jac"] )
		{
			// declare independent variables
			Independent(a1x);
			//
			// AD computation of y
			a1vector a1y(1);
			CppAD::sparse_hes_fun<a1double>(n, a1x, row, col, order, a1y);
			//
			// create function object f : X -> Y
			fun.Dependent(a1x, a1y);
			//
			if( global_option["optimize"] )
				fun.optimize(optimize_options);
			//
			// skip comparison operators
			fun.compare_change_count(0);
			//
			// fun corresonds to f(x)
			return;
		}
		// declare independent variables for f(x)
		a2vector a2x(n);
		for(size_t j = 0; j < n; j++)
			a2x[j] = a1x[j];
		Independent(a2x);
		//
		// a2double computation of y
		a2vector a2y(1);
		CppAD::sparse_hes_fun<a2double>(n, a2x, row, col, order, a2y);
		//
		// create function object corresponding to y = f(x)
		CppAD::ADFun<a1double> a1f;
		a1f.Dependent(a2x, a2y);
		//
		// declare independent variables for g(x)
		Independent(a1x);
		//
		// a1double computation of z
		a1vector a1w(1), a1z(n);
		a1w[0] = 1.0;
		a1f.Forward(0, a1x);
		a1z = a1f.Reverse(1, a1w);
		//
		// create function object z = g(x) = f'(x)
		fun.Dependent(a1x, a1z);
		//
		if( global_option["optimize"] )
			fun.optimize(optimize_options);
		//
		// skip comparison operators
		fun.compare_change_count(0);
		//
		// fun corresonds to g(x)
		return;
	}
	// ------------------------------------------------------------------------
	void calc_sparsity(
		sparsity_pattern&      sparsity ,
		CppAD::ADFun<double>&  fun      )
	{
		size_t n = fun.Domain();
		size_t m = fun.Range();
		//
		bool transpose     = false;
		//
		if( global_option["subsparsity"] )
		{	CPPAD_ASSERT_UNKNOWN( global_option["hes2jac"] )
			CPPAD_ASSERT_UNKNOWN( n == m );
			b_vector select_domain(n), select_range(m);
			for(size_t j = 0; j < n; ++j)
				select_domain[j] = true;
			for(size_t i = 0; i < m; ++i)
				select_range[i] = true;
			//
			// fun corresponds to g(x)
			fun.subgraph_sparsity(
				select_domain, select_range, transpose, sparsity
			);
			return;
		}
		bool dependency    = false;
		bool reverse       = global_option["revsparsity"];
		bool internal_bool = global_option["boolsparsity"];
		//
		if( ! global_option["hes2jac"] )
		{	// fun corresponds to f(x)
			//
			CPPAD_ASSERT_UNKNOWN( m == 1 );
			//
			b_vector select_range(m);
			select_range[0] = true;
			//
			if( reverse )
			{	sparsity_pattern identity;
				identity.resize(n, n, n);
				for(size_t k = 0; k < n; k++)
					identity.set(k, k, k);
				fun.for_jac_sparsity(
					identity, transpose, dependency, internal_bool, sparsity
				);
				fun.rev_hes_sparsity(
					select_range, transpose, internal_bool, sparsity
				);
			}
			else
			{	b_vector select_domain(n);
				for(size_t j = 0; j < n; j++)
					select_domain[j] = true;
				fun.for_hes_sparsity(
					select_domain, select_range, internal_bool, sparsity
				);
			}
			return;
		}
		// fun correspnds to g(x)
		CPPAD_ASSERT_UNKNOWN( m == n );
		//
		// sparsity pattern for identity matrix
		sparsity_pattern eye;
		eye.resize(n, n, n);
		for(size_t k = 0; k < n; k++)
			eye.set(k, k, k);
		//
		if( reverse )
		{	fun.rev_jac_sparsity(
				eye, transpose, dependency, internal_bool, sparsity
			);
		}
		else
		{	fun.for_jac_sparsity(
				eye, transpose, dependency, internal_bool, sparsity
			);
		}
		return;
	}
	// ------------------------------------------------------------------------
	size_t calc_hessian(
		d_vector&               hessian  ,
		const d_vector&         x        ,
		sparse_matrix&          subset   ,
		const sparsity_pattern& sparsity ,
		CppAD::sparse_jac_work& jac_work ,
		CppAD::sparse_hes_work& hes_work ,
		CppAD::ADFun<double>&   fun      )
	{	size_t n_sweep;
		//
		if( ! global_option["hes2jac"] )
		{	// fun corresponds to f(x)
			//
			// coloring method
			std::string coloring = "cppad.symmetric";
# if CPPAD_HAS_COLPACK
			if( global_option["colpack"] )
				coloring = "colpack.symmetric";
# endif
			// only one function component
			d_vector w(1);
			w[0] = 1.0;
			//
			// compute hessian
			n_sweep = fun.sparse_hes(
				x, w, subset, sparsity, coloring, hes_work
			);
		}
		else
		{	// fun corresponds to g(x)
			//
			if( global_option["subgraph"] )
			{	fun.subgraph_jac_rev(x, subset);
				n_sweep = 0;
			}
			else
			{
				//
				// coloring method
				std::string coloring = "cppad";
# if CPPAD_HAS_COLPACK
				if( global_option["colpack"] )
					coloring = "colpack";
# endif
				size_t group_max = 1;
				n_sweep = fun.sparse_jac_for(
					group_max, x, subset, sparsity, coloring, jac_work
				);
			}
		}
		// return result
		const d_vector& val( subset.val() );
		size_t nnz = subset.nnz();
		for(size_t k = 0; k < nnz; k++)
			hessian[k] = val[k];
		//
		return n_sweep;
	}
}

bool link_sparse_hessian(
	size_t                           size     ,
	size_t                           repeat   ,
	const CppAD::vector<size_t>&     row      ,
	const CppAD::vector<size_t>&     col      ,
	CppAD::vector<double>&           x        ,
	CppAD::vector<double>&           hessian  ,
	size_t&                          n_sweep  )
{
	// --------------------------------------------------------------------
	// check global options
	const char* valid[] = {
		"memory", "onetape", "optimize", "hes2jac", "subgraph",
# if CPPAD_HAS_COLPACK
		"boolsparsity", "revsparsity", "subsparsity", "colpack"
# else
		"boolsparsity", "revsparsity"
# endif
	};
	size_t n_valid = sizeof(valid) / sizeof(valid[0]);
	typedef std::map<std::string, bool>::iterator iterator;
	//
	for(iterator itr=global_option.begin(); itr!=global_option.end(); ++itr)
	{	if( itr->second )
		{	bool ok = false;
			for(size_t i = 0; i < n_valid; i++)
				ok |= itr->first == valid[i];
			if( ! ok )
				return false;
		}
	}
	if( global_option["subsparsity"] )
	{	if( global_option["boolsparsity"] || global_option["revsparsity"] )
			return false;
		if( ! global_option["hes2jac"] )
			return false;
	}
	if( global_option["subgraph"] )
	{	if( ! global_option["hes2jac"] )
			return false;
	}
	// -----------------------------------------------------------------------
	// setup
	size_t n = size;          // number of independent variables
	CppAD::ADFun<double> fun; // AD function object used to calculate Hessian
	//
	// declare sparsity pattern
	sparsity_pattern sparsity;
	//
	// declare subset where Hessian is evaluated
	sparsity_pattern subset_pattern;
	size_t nr  = n;
	size_t nc  = n;
	size_t nnz = row.size();
	subset_pattern.resize(nr, nc, nnz);
	for(size_t k = 0; k < nnz; k++)
		subset_pattern.set(k, row[k], col[k]);
	sparse_matrix subset( subset_pattern );
	//
	// structures that holds some of the work done by sparse_jac, sparse_hes
	CppAD::sparse_jac_work jac_work;
	CppAD::sparse_hes_work hes_work;

	// -----------------------------------------------------------------------
	if( ! global_option["onetape"] ) while(repeat--)
	{	// choose a value for x
		CppAD::uniform_01(n, x);
		//
		// create f(x)
		create_fun(x, row, col, fun);
		//
		// calculate the sparsity pattern for Hessian of f(x)
		calc_sparsity(sparsity, fun);
		//
		// calculate the Hessian at this x
		jac_work.clear(); // wihtout work from previous calculation
		hes_work.clear();
		n_sweep = calc_hessian(
			hessian, x, subset, sparsity, jac_work, hes_work, fun
		);
	}
	else
	{	// choose a value for x
		CppAD::uniform_01(n, x);
		//
		// create f(x)
		create_fun(x, row, col, fun);
		//
		// calculate the sparsity pattern for Hessian of f(x)
		calc_sparsity(sparsity, fun);
		//
		while(repeat--)
		{	// choose a value for x
			CppAD::uniform_01(n, x);
			//
			// calculate this Hessian at this x
			n_sweep = calc_hessian(
				hessian, x, subset, sparsity, jac_work, hes_work, fun
			);
		}
	}
	return true;
}
/* %$$
$end
*/
